Nurses’ experiences of using falls alarms in subacute care: A qualitative study
Bibliographic record
Abstract
Bed and chair alarms have been included in many multifaceted falls prevention interventions. None of the randomised trials of falls alarms as sole interventions have showed significant effect on falls or falls with injury. Further, use of bed and chair alarms did not change patients' fear of falling, length of hospital stay, functional status, discharge destination or health related quality of life. The aim of this study was to explore nurses' experiences of using bed and chair alarms. A qualitative descriptive study using semi-structured interviews with a purposive sample of 12 nurses was conducted on a 32-bed Geriatric Evaluation and Management ward in Melbourne, Australia. Participants were interviewed between 27 January and 12 March 2021.Transcribed audio-recordings of interviews were analysed using inductive thematic analysis. NVIVO 12.6 was used to manage the study data. Three major themes and four subthemes were constructed from the data: i) negative impacts of falls alarms (subthemes: noisy technology, imperfect technology), ii) juggling the safety-risk conflict, and iii) negotiating falls alarm use (subthemes: nurse decision making and falls alarm overuse). Nurses' experience of using falls alarms was predominantly negative and there was tension between falls alarms having limited impact on patient safety and risks associated with their use. Nurses described a need to support nurse decision making related to falls alarms use in practice and policy, and a desire to be empowered to manage falls risk in other ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".